AGRO: An Adaptive Gold Rush Optimizer with Dynamic Strategy Selection

In this paper, we propose a metaheuristic optimization algorithm called Adaptive Gold Rush Optimizer (AGRO), a substantial evolution of the original Gold Rush Optimizer (GRO). Unlike the standard GRO, which relies on fixed probabilities in the strategy selection process, AGRO utilizes a novel adaptive mechanism that prioritizes strategies improving solution quality. This adaptive component, which can be applied to any optimization algorithm with fixed probabilities in the strategy selection, adjusts the probabilities of the three core search strategies of GRO (Migration, Collaboration, and Panning), in real time, rewarding those that successfully improve solution quality. Furthermore, AGRO introduces fundamental modifications to the search equations, eliminating the inherent attraction towards the zero coordinates, while explicitly incorporating objective function values to guide prospectors towards promising regions. Experimental results demonstrate that AGRO is highly competitive against ten state-of-the-art algorithms on the twenty-three classical benchmark functions, the CEC2017, and the CEC2019 datasets, offering robust performance across diverse problem landscapes.